Enhancing Hydrocarbon Permeability After Hydraulic Fracturing: Laboratory Evaluations of Shut-Ins and Surfactant Additives
Bibliographic record
Abstract
Summary Fracturing-fluid loss into the formation can potentially damage hydrocarbon production in shale or other tight reservoirs. Well shut-ins are commonly used in the field to dissipate the lost water into the matrix near fracture faces. Borrowing from ideas in chemical enhanced oil recovery (CEOR), surfactants have potential to reduce the effect of fracturing-fluid loss on hydrocarbon permeability in the matrix. Unconventional tight reservoirs can differ significantly from one another, which could make the use of these techniques effective in some cases but not in others. We present an experimental investigation dependent on a coreflood sequence that simulates fluid invasion, flowback, and hydrocarbon production from hydraulically fractured reservoirs. We compare the benefits of shut-ins and reduction in interfacial tension (IFT) by surfactants for hydrocarbon permeability for different initial reservoir conditions (IRCs). From this work, we identify the mechanism responsible for the permeability reduction in the matrix, and we suggest criteria that can be used to optimize fracturing-fluid additives and/or manage flowback operations to enhance hydrocarbon production from unconventional tight reservoirs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".